{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/program-synthesis/papers/4","list_of":"/task/program-synthesis","task":"Program Synthesis","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":4,"pages_in_order":5,"rows_per_page":100,"rows":[301,400],"of":423,"counts":{"archive_papers_tagged":423,"with_a_code_link":179,"where_syntology_ran_a_sample":69,"not_listed_spam_title":0,"listed":423,"listed_where_code_ran":69,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":52,"every_run_a_failure_of_syntologys_instrument":17,"listed_with_a_run_with_no_instrument_failure":52,"listed_every_run_a_failure_of_syntologys_instrument":17,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/program-synthesis","prev":"/task/program-synthesis/papers/3","next":"/task/program-synthesis/papers/5","papers":[{"url":null,"slug":"autotsg-learning-and-synthesis-for-incident","title":"AutoTSG: Learning and Synthesis for Incident Troubleshooting","date":"2022-05-26","arxiv_id":"2205.13457","repositories_listed":0,"syntology":null},{"url":null,"slug":"autoformalization-with-large-language-models","title":"Autoformalization with Large Language Models","date":"2022-05-25","arxiv_id":"2205.12615","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-program-synthesis-with-query-1","title":"Neural Program Synthesis with Query","date":"2022-05-08","arxiv_id":"2205.07857","repositories_listed":0,"syntology":null},{"url":null,"slug":"example-based-synthesis-of-static-analysis","title":"Example-based Synthesis of Static Analysis Rules","date":"2022-04-19","arxiv_id":"2204.08643","repositories_listed":0,"syntology":null},{"url":null,"slug":"population-diversity-leads-to-short-running","title":"Population Diversity Leads to Short Running Times of Lexicase Selection","date":"2022-04-13","arxiv_id":"2204.06461","repositories_listed":0,"syntology":null},{"url":null,"slug":"landmarks-and-regions-a-robust-approach-to","title":"Landmarks and Regions: A Robust Approach to Data Extraction","date":"2022-04-11","arxiv_id":"2204.05021","repositories_listed":0,"syntology":null},{"url":null,"slug":"compositional-generalization-and","title":"Compositional Generalization and Decomposition in Neural Program Synthesis","date":"2022-04-07","arxiv_id":"2204.03758","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-pragmatic-program-synthesis-with","title":"Efficient Pragmatic Program Synthesis with Informative Specifications","date":"2022-04-05","arxiv_id":"2204.02495","repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-genetic-improvement-scaling","title":"Iterative Genetic Improvement: Scaling Stochastic Program Synthesis","date":"2022-02-26","arxiv_id":"2202.13040","repositories_listed":0,"syntology":null},{"url":null,"slug":"sapientml-synthesizing-machine-learning","title":"SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions","date":"2022-02-18","arxiv_id":"2202.10451","repositories_listed":0,"syntology":null},{"url":null,"slug":"mwp-bert-numeracy-augmented-pre-training-for","title":"MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"differentiable-synthesis-of-program","title":"Differentiable Synthesis of Program Architectures","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"stress-rules-from-surface-forms-experiments","title":"Stress Rules from Surface Forms: Experiments with Program Synthesis","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-visual-analogies-using-neural","title":"Solving Visual Analogies Using Neural Algorithmic Reasoning","date":"2021-11-19","arxiv_id":"2111.10361","repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-linear-algebra-by-program-synthesis","title":"Solving Linear Algebra by Program Synthesis","date":"2021-11-16","arxiv_id":"2111.08171","repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-linear-algebra-by-program-synthesis-1","title":"Solving Linear Algebra by Program Synthesis","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-probability-and-statistics-problems","title":"Solving Probability and Statistics Problems by Program Synthesis","date":"2021-11-16","arxiv_id":"2111.08267","repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-probability-and-statistics-problems-1","title":"Solving Probability and Statistics Problems by Program Synthesis","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"choose-your-programming-copilot-a-comparison","title":"Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programming","date":"2021-11-15","arxiv_id":"2111.07875","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-novel-concept-learning-for-semantic","title":"Few-Shot Novel Concept Learning for Semantic Parsing","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-guided-bidirectional-program-search","title":"Neural-guided, Bidirectional Program Search for Abstraction and Reasoning","date":"2021-10-22","arxiv_id":"2110.11536","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthesizing-optimal-parallelism-placement","title":"Synthesizing Optimal Parallelism Placement and Reduction Strategies on Hierarchical Systems for Deep Learning","date":"2021-10-20","arxiv_id":"2110.10548","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-program-synthesis-and-inductive-logic","title":"Using Program Synthesis and Inductive Logic Programming to solve Bongard Problems","date":"2021-10-19","arxiv_id":"2110.09947","repositories_listed":0,"syntology":null},{"url":null,"slug":"top-3-in-fg-2021-families-in-the-wild-kinship","title":"Solving the Families In the Wild Kinship Verification Challenge by Program Synthesis","date":"2021-10-13","arxiv_id":"2110.07020","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-genetic-programming-approach-to-zero-shot","title":"A Genetic Programming Approach To Zero-Shot Neural Architecture Ranking","date":"2021-10-08","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-robustness-of-program-synthesis","title":"Adversarial Robustness of Program Synthesis Models","date":"2021-10-08","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"are-transformers-all-that-karel-needs","title":"Are Transformers All That Karel Needs?","date":"2021-10-08","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"autocoder-leveraging-transformers-for","title":"AutoCoder: Leveraging Transformers for Automatic Code Synthesis","date":"2021-10-08","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"autumnsynth-synthesis-of-reactive-programs","title":"AutumnSynth: Synthesis of Reactive Programs with Structured Latent State","date":"2021-10-08","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-physical-imperceptible-adversarial","title":"Towards Physical, Imperceptible Adversarial Attacks via Adversarial Programs","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"parallel-constraint-driven-inductive-logic","title":"Parallel Constraint-Driven Inductive Logic Programming","date":"2021-09-15","arxiv_id":"2109.07132","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-program-inference-a-marriage-of","title":"Multi-modal Program Inference: a Marriage of Pre-trainedLanguage Models and Component-based Synthesis","date":"2021-09-03","arxiv_id":"2109.02445","repositories_listed":0,"syntology":null},{"url":null,"slug":"recent-developments-in-program-synthesis-with","title":"Recent Developments in Program Synthesis with Evolutionary Algorithms","date":"2021-08-27","arxiv_id":"2108.12227","repositories_listed":0,"syntology":null},{"url":null,"slug":"satisfiability-and-synthesis-modulo-oracles","title":"Satisfiability and Synthesis Modulo Oracles","date":"2021-07-28","arxiv_id":"2107.13477","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-language-to-learn-program","title":"Leveraging Language to Learn Program Abstractions and Search Heuristics","date":"2021-06-18","arxiv_id":"2106.11053","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpreting-expert-annotation-differences-in","title":"Interpreting Expert Annotation Differences in Animal Behavior","date":"2021-06-11","arxiv_id":"2106.06114","repositories_listed":0,"syntology":null},{"url":null,"slug":"psb2-the-second-program-synthesis-benchmark","title":"PSB2: The Second Program Synthesis Benchmark Suite","date":"2021-06-10","arxiv_id":"2106.06086","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-execution-for-neural-program-synthesis-1","title":"Latent Execution for Neural Program Synthesis Beyond Domain-Specific Languages","date":"2021-05-21","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-decision-based-adversarial-attacks","title":"Automated Decision-based Adversarial Attacks","date":"2021-05-09","arxiv_id":"2105.03931","repositories_listed":0,"syntology":null},{"url":null,"slug":"inductive-program-synthesis-over-noisy","title":"Inductive Program Synthesis over Noisy Datasets using Abstraction Refinement Based Optimization","date":"2021-04-27","arxiv_id":"2104.13315","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-code-generation-a-survey-and-lessons","title":"Toward Code Generation: A Survey and Lessons from Semantic Parsing","date":"2021-04-26","arxiv_id":"2105.03317","repositories_listed":0,"syntology":null},{"url":null,"slug":"geometry-of-program-synthesis-1","title":"Geometry of Program Synthesis","date":"2021-03-30","arxiv_id":"2103.16080","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-program-synthesis-over-noisy-data","title":"Program Synthesis Over Noisy Data with Guarantees","date":"2021-03-08","arxiv_id":"2103.05030","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hint-from-arithmetic-on-systematic","title":"A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics","date":"2021-03-02","arxiv_id":"2103.01403","repositories_listed":0,"syntology":null},{"url":null,"slug":"refinement-type-directed-search-for-meta","title":"Refinement Type Directed Search for Meta-Interpretive-Learning of Higher-Order Logic Programs","date":"2021-02-18","arxiv_id":"2102.12553","repositories_listed":0,"syntology":null},{"url":null,"slug":"report-of-the-workshop-on-program-synthesis","title":"Report of the Workshop on Program Synthesis for Scientific Computing","date":"2021-02-02","arxiv_id":"2102.01687","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-differentially-private-mechanisms","title":"Learning Differentially Private Mechanisms","date":"2021-01-04","arxiv_id":"2101.00961","repositories_listed":0,"syntology":null},{"url":null,"slug":"fcr-flow-chart-recognition-network-for","title":"FCR: Flow Chart Recognition Network for Program Synthesis","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neurosymbolic-deep-generative-models-for","title":"Neurosymbolic Deep Generative Models for Sequence Data with Relational Constraints","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"visible-and-invisible-causal-variable","title":"Visible and Invisible: Causal Variable Learning and its Application in a Cancer Study","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"representing-partial-programs-with-blended-1","title":"Representing Partial Programs with Blended Abstract Semantics","date":"2020-12-23","arxiv_id":"2012.12964","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-programmer-discrete-latent-codes-for-1","title":"Latent Programmer: Discrete Latent Codes for Program Synthesis","date":"2020-12-01","arxiv_id":"2012.00377","repositories_listed":0,"syntology":null},{"url":null,"slug":"plans-neuro-symbolic-program-learning-from","title":"PLANS: Neuro-Symbolic Program Learning from Videos","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-plane-program-induction-with-3d-box-1","title":"Multi-Plane Program Induction with 3D Box Priors","date":"2020-11-19","arxiv_id":"2011.10007","repositories_listed":0,"syntology":null},{"url":null,"slug":"grcnn-graph-recognition-convolutional-neural","title":"GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing Programs from Flow Charts","date":"2020-11-11","arxiv_id":"2011.05980","repositories_listed":0,"syntology":null},{"url":null,"slug":"dreaming-with-arc","title":"Dreaming with ARC","date":"2020-10-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-generation-of-executable-cross","title":"Automated Generation of Executable Cross-Language Background Knowledge","date":"2020-10-13","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"goal-directed-generation-of-discrete","title":"Goal-directed Generation of Discrete Structures with Conditional Generative Models","date":"2020-10-05","arxiv_id":"2010.02311","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-neural-program-synthesis-from-1","title":"Optimal Neural Program Synthesis from Multimodal Specifications","date":"2020-10-04","arxiv_id":"2010.01678","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-synthetic-datasets-for-neural","title":"Adversarial Synthetic Datasets for Neural Program Synthesis","date":"2020-09-28","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"type-driven-neural-programming-by-example","title":"Type-driven Neural Programming by Example","date":"2020-08-28","arxiv_id":"2008.12613","repositories_listed":0,"syntology":null},{"url":null,"slug":"process-discovery-for-structured-program","title":"Process Discovery for Structured Program Synthesis","date":"2020-08-13","arxiv_id":"2008.05804","repositories_listed":0,"syntology":null},{"url":null,"slug":"bustle-bottom-up-program-synthesis-through","title":"BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration","date":"2020-07-28","arxiv_id":"2007.14381","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-learning-from-demonstration","title":"Semi-supervised Learning From Demonstration Through Program Synthesis: An Inspection Robot Case Study","date":"2020-07-23","arxiv_id":"2007.12500","repositories_listed":0,"syntology":null},{"url":null,"slug":"programming-by-rewards","title":"Programming by Rewards","date":"2020-07-14","arxiv_id":"2007.06835","repositories_listed":0,"syntology":null},{"url":null,"slug":"program-synthesis-with-pragmatic","title":"Program Synthesis with Pragmatic Communication","date":"2020-07-09","arxiv_id":"2007.05060","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-web-based-procedures-by-reasoning","title":"Learning Web-based Procedures by Reasoning over Explanations and Demonstrations in Context","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"information-theoretic-user-interaction","title":"Information-theoretic User Interaction: Significant Inputs for Program Synthesis","date":"2020-06-22","arxiv_id":"2006.12638","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-program-synthesis-with-a","title":"Neural Program Synthesis with a Differentiable Fixer","date":"2020-06-19","arxiv_id":"2006.10924","repositories_listed":0,"syntology":null},{"url":null,"slug":"ireen-iterative-reverse-engineering-of-black","title":"IReEn: Reverse-Engineering of Black-Box Functions via Iterative Neural Program Synthesis","date":"2020-06-18","arxiv_id":"2006.10720","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-large-logic-programs-by-going-beyond","title":"Learning large logic programs by going beyond entailment","date":"2020-04-21","arxiv_id":"2004.09855","repositories_listed":0,"syntology":null},{"url":null,"slug":"creating-synthetic-datasets-via-evolution-for","title":"Creating Synthetic Datasets via Evolution for Neural Program Synthesis","date":"2020-03-23","arxiv_id":"2003.10485","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-program-synthesis-for-images","title":"Unsupervised Program Synthesis for Images By Sampling Without Replacement","date":"2020-01-27","arxiv_id":"2001.10119","repositories_listed":0,"syntology":null},{"url":null,"slug":"counterexample-guided-neural-synthesis","title":"CounterExample Guided Neural Synthesis","date":"2020-01-25","arxiv_id":"2001.09245","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-neural-guided-program-synthesis-for","title":"Towards Neural-Guided Program Synthesis for Linear Temporal Logic Specifications","date":"2019-12-31","arxiv_id":"1912.13430","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-datasets-for-neural-program-1","title":"Synthetic Datasets for Neural Program Synthesis","date":"2019-12-27","arxiv_id":"1912.12345","repositories_listed":0,"syntology":null},{"url":null,"slug":"constraint-based-learning-of-phonological","title":"Constraint-based Learning of Phonological Processes","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-causal-inference-via-probabilistic","title":"Bayesian causal inference via probabilistic program synthesis","date":"2019-10-30","arxiv_id":"1910.14124","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-probabilistic-surrogate-networks-for","title":"Probabilistic Surrogate Networks for Simulators with Unbounded Randomness","date":"2019-10-25","arxiv_id":"1910.11950","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-program-synthesis-by-self-learning-1","title":"Neural Program Synthesis By Self-Learning","date":"2019-10-13","arxiv_id":"1910.05865","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-sizing-of-stand-alone-solar-pv","title":"Optimal Sizing of Stand-alone Solar PV Systems via Automated Formal Synthesis","date":"2019-09-28","arxiv_id":"1909.13139","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-generation-of-programming-puzzles","title":"ADAPTIVE GENERATION OF PROGRAMMING PUZZLES","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-target-augmentation-for-effective","title":"Iterative Target Augmentation for Effective Conditional Generation","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"natural-to-formal-language-generation-using","title":"Natural- to formal-language generation using Tensor Product Representations","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"netsyn-neural-evolutionary-technique-to","title":"Learning Fitness Functions for Machine Programming","date":"2019-08-22","arxiv_id":"1908.08783","repositories_listed":0,"syntology":null},{"url":null,"slug":"imitation-projected-policy-gradient-for","title":"Imitation-Projected Programmatic Reinforcement Learning","date":"2019-07-11","arxiv_id":"1907.05431","repositories_listed":0,"syntology":null},{"url":null,"slug":"write-execute-assess-program-synthesis-with-a","title":"Write, Execute, Assess: Program Synthesis with a REPL","date":"2019-06-09","arxiv_id":"1906.04604","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-shot-information-extraction-from-document","title":"One-shot Information Extraction from Document Images using Neuro-Deductive Program Synthesis","date":"2019-06-06","arxiv_id":"1906.02427","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600163","title":"Synthesizing Datalog Programs Using Numerical Relaxation","date":"2019-06-01","arxiv_id":"1906.00163","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-and-combining-lexicase-selection","title":"Comparing and Combining Lexicase Selection and Novelty Search","date":"2019-05-22","arxiv_id":"1905.09374","repositories_listed":0,"syntology":null},{"url":null,"slug":"execution-guided-neural-program-synthesis","title":"Execution-Guided Neural Program Synthesis","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-a-meta-solver-for-syntax-guided","title":"Learning a Meta-Solver for Syntax-Guided Program Synthesis","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-program-planner-for-structured","title":"Neural Program Planner for Structured Predictions","date":"2019-03-23","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-neurosymbolic-generative-models-via","title":"Learning Neurosymbolic Generative Models via Program Synthesis","date":"2019-01-24","arxiv_id":"1901.08565","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-semantic-parsing","title":"A Survey on Semantic Parsing","date":"2018-12-03","arxiv_id":"1812.00978","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-neural-program-synthesis-with","title":"Improving Neural Program Synthesis with Inferred Execution Traces","date":"2018-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"aint-nobody-got-time-for-coding-structure","title":"Ain't Nobody Got Time For Coding: Structure-Aware Program Synthesis From Natural Language","date":"2018-10-23","arxiv_id":"1810.09717","repositories_listed":0,"syntology":null},{"url":null,"slug":"inference-over-programs-that-make-predictions","title":"Inference Over Programs That Make Predictions","date":"2018-10-02","arxiv_id":"1810.01190","repositories_listed":0,"syntology":null},{"url":null,"slug":"semregex-a-semantics-based-approach-for","title":"SemRegex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"novel-positional-encodings-to-enable-tree-1","title":"Novel positional encodings to enable tree-structured transformers","date":"2018-09-27","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"f5d44b9f47d7143dda296a31b3728dc42e0bb30bbd549b603c628fa922b46047","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}